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Titlebook: MCMC from Scratch; A Practical Introduc Masanori Hanada,So Matsuura Textbook 2022 The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 16:03:53 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱MCMC from Scratch
副標題A Practical Introduc
編輯Masanori Hanada,So Matsuura
視頻videohttp://file.papertrans.cn/621/620089/620089.mp4
概述Explains the fundamentals of MCMC and important algorithms without assuming advanced knowledge of math and programming.Contains many examples, exercises with solutions, and codes.Equips readers to wri
圖書封面Titlebook: MCMC from Scratch; A Practical Introduc Masanori Hanada,So Matsuura Textbook 2022 The Editor(s) (if applicable) and The Author(s), under ex
描述.This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC)? without assuming advanced knowledge of mathematics and programming. MCMC is? a powerful technique that can be used to integrate complicated functions or to handle? complicated probability distributions. MCMC is frequently used in diverse fields where? statistical methods are important – e.g. Bayesian statistics, quantum physics, machine? learning, computer science, computational biology, and mathematical economics. This? book aims to equip readers with a sound understanding of MCMC and enable them? to write simulation codes by themselves.? ..The content consists of six chapters. Following Chap. 2, which introduces readers?to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents? the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through? the simple example of the Metropolis algorithm. In turn, Chap. 5explains the HMC? algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing? their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC.? Including a wealth of examples and exercises with solutions, as well as sampl
出版日期Textbook 2022
關(guān)鍵詞Markov Chain Monte Carlo; MCMC; Numerical simulation; Computational science; Metropolis algorithm; HMC al
版次1
doihttps://doi.org/10.1007/978-981-19-2715-7
isbn_softcover978-981-19-2717-1
isbn_ebook978-981-19-2715-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 21:55:54 | 只看該作者
Masanori Hanada,So Matsuurawere compared to check which one seems to have greater efficiency when being handled by educators. It was concluded that the Augmania? software would be the best option for these professionals to create AR projects and activities to be performed in their classrooms, since it presents a simple and in
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Masanori Hanada,So Matsuura learning, or form idiosyncratic links with existing ideas that seem relevant from the student’s perspective. An engaging chemistry teacher, who provides students with a range of relevant learning activities, will inevitably produce active learning in the sense of the mental construction of new know
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Masanori Hanada,So Matsuura learning, or form idiosyncratic links with existing ideas that seem relevant from the student’s perspective. An engaging chemistry teacher, who provides students with a range of relevant learning activities, will inevitably produce active learning in the sense of the mental construction of new know
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omplicated statistical analysis can show that teachers should be aware that girls need more attention in chemistry learning than boys. Teachers should extrinsically motivate using macroscopic level of chemical concepts and put those concepts into the context for those students who are low achievers
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nd Metropolis-Hastings algorithm, discussing? their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC.? Including a wealth of examples and exercises with solutions, as well as sampl978-981-19-2717-1978-981-19-2715-7
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發(fā)表于 2025-3-23 08:05:00 | 只看該作者
Textbook 2022ropolis algorithm. In turn, Chap. 5explains the HMC? algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing? their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC.? Including a wealth of examples and exercises with solutions, as well as sampl
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